CPG Vertical AI

Acquire · Refine · Orchestrate
‍The Commerce SuperIntelligence Brain

Grivy’s Vertical AI Model is a CPG + Retail-specific large language model designed to turn messy, real-world commerce signals into precise decisions that move revenue. It sits on top of the Grivy Growth Platform and learns from every interaction across packaging, stores, media, and purchases—so brands and retailers stop guessing and start operating with a continuously improving intelligence layer.

The Grivy Vertical AI for CPG & Retail is one commerce brain, three intelligence stages.

Core Components

1. Acquire — Build the commerce knowledge base

Inputs	What We Capture
Product Signals	Product graph, SKU catalogs, attributes, pack sizes, pricing, promo mechanics, claims, nutrition, competitor mapping.
Retail Signals	Store/location graph, channel hierarchy, planograms, inventory proxies, shelf visibility, retail media touchpoints.
Consumer Signals	Identity graph, Consent-based IDs, engagement events, mission behavior, coupon redemptions, loyalty journeys.
Transaction Signals	Commerce graph, POS line items, receipt reads, basket composition, frequency, trade-up/down behavior.

Key tech

Product graph + location graph + transaction graph + identity graph unified under a privacy-safe ontology.

Why it matters?

General AI is blind to the real structure of commerce. This stage gives the model a grounded “world model” of how CPG actually moves through channels.

2. Refine — Turn chaos into intelligence

Engines	What They Produce
Commerce Ontology & Reasoning	Normalized entities (SKU ↔ brand ↔ category ↔ retailer ↔ store ↔ shopper) and cause-effect links.
Data Anthropology Pipeline	Cleans dirty POS/receipt data, resolves SKU ambiguity, de-duplicates identities, fixes time/location errors.
Retrieval + Grounding Layer (RAG)	The model answers using verified internal truths (plans, promos, store lists, POS facts), not vibes.

Key tech

Translation layer, vector search over product knowledge, rules + probabilistic reconciliation, constrained generation with source grounding.

Why it matters?

This is where “AI talk” becomes AI truth—translated, structured, reliable, and measurable enough for CFO-grade decisions.

3. Orchestrate — Convert intelligence into actions

Orchestration Rails	Outcomes Delivered
Agent Workflows for Growth	Auto-generate missions, offers, creatives, and segmentation plans tuned per retailer/channel.
Trade & Retail Copilots	“Which 200 stores should we activate next week?” “What’s the best promo for SKU X in East Java?”
Budget + Inventory Alignment	Suggest media allocations, sell-in pushes, and replenishment triggers based on predicted velocity.

Key tech

Multi-agent execution (planner → analyst → creator → operator), policy constraints, evaluation loops with live POS feedback.

Why it matters?

Insight doesn’t live in slides. It becomes operational automation—briefs, audiences, missions, store targets, and measurable uplift.

1. Acquire — Build the commerce knowledge base

Inputs	What we capture
Product Signals	Product graph, SKU catalogs, attributes, pack sizes, pricing, promo mechanics, claims, nutrition, competitor mapping.
Retail Signals	Store/location graph, channel hierarchy, planograms, inventory proxies, shelf visibility, retail media touchpoints.
Consumer Signals	Identity graph, Consent-based IDs, engagement events, mission behavior, coupon redemptions, loyalty journeys.
Transaction Signals	Commerce graph, POS line items, receipt reads, basket composition, frequency, trade-up/down behavior.

Key tech

Product graph + location graph + transaction graph + identity graph unified under a privacy-safe ontology.

Why it matters?

General AI is blind to the real structure of commerce. This stage gives the model a grounded “world model” of how CPG actually moves through channels.

2. Refine — Turn chaos into intelligence

Engines	What they produce
Commerce Ontology & Reasoning	Normalized entities (SKU ↔ brand ↔ category ↔ retailer ↔ store ↔ shopper) and cause-effect links.
Data Anthropology Pipeline	Cleans dirty POS or receipt data, resolves SKU ambiguity, de-duplicates identities, fixes time or location errors.
Retrieval + Grounding Layer (RAG)	The model answers using verified internal truths (plans, promos, store lists, POS facts), not vibes.

Key tech

Translation layer, vector search over product knowledge, rules + probabilistic reconciliation, constrained generation with source grounding.

Why it matters?

This is where “AI talk” becomes AI truth—translated, structured, reliable, and measurable enough for CFO-grade decisions.

3. Activation — Reach Your Audience Everywhere

The Activation Rails is where you put your audience data to work by delivering personalized experiences across all your marketing channels

Activation Rails	Outcomes Delivered
Agent Workflows for Growth	Auto-generate missions, offers, creatives, and segmentation plans tuned per retailer/channel.
Trade & Retail Copilots	“Which 200 stores should we activate next week?” “What’s the best promo for SKU X in East Java?”
Budget + Inventory Alignment	Suggest media allocations, sell-in pushes, and replenishment triggers based on predicted velocity.

Key tech

Multi-agent execution (planner → analyst → creator → operator), policy constraints, evaluation loops with live POS feedback.

Why it matters?

Insight doesn’t live in slides. It becomes operational automation—briefs, audiences, missions, store targets, and measurable uplift.

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